Signature discovery from omics data
Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets.
System | Target | Derivation | Build status |
---|---|---|---|
x86_64-linux | /gnu/store/z429d6f44m7vmx95sg8cncr03vcadzqz-r-biosigner-1.14.0.drv | ||
mips64el-linux | /gnu/store/lmz9ps17hlgc6nnybnqib6kbd388wgsd-r-biosigner-1.14.0.drv | ||
i686-linux | /gnu/store/lmr39rz7f130ak5nzwvvcs1nvkl5s1n6-r-biosigner-1.14.0.drv | ||
armhf-linux | /gnu/store/rgk9ws9m2vam2ws799z69x3ydkxkn49s-r-biosigner-1.14.0.drv | ||
aarch64-linux | /gnu/store/5c6shqbrgzr4v7l0i8cn37b1q9z222if-r-biosigner-1.14.0.drv |
Linter | Message | Location |
---|---|---|
description Validate package descriptions | use @code or similar ornament instead of quotes |